Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy by enhancing diagnostic accuracy, improving early detection, and enabling personalized treatment planning. AI applications, such as computer-aided detection (CADe) and computer-aided diagnosis (CADx), have significantly advanced GI endoscopy, particularly in conditions with low diagnostic sensitivity, like indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks (CNNs) improve diagnostic processes when integrated with cholangioscopy or endoscopic ultrasound, especially in detecting malignant biliary strictures and cholangiocarcinoma. AI's ability to analyze complex image data and provide real-time feedback streamlines endoscopic procedures, reduces the need for invasive biopsies, and decreases adverse events. However, challenges such as data quality and overfitting remain, requiring further research and validation. AI has shown promise in detecting early-stage gastric cancer, identifying Helicobacter pylori infections, and improving the detection of colorectal polyps and cancer. Studies indicate that AI systems can achieve high sensitivity and specificity in detecting gastric cancer and predicting H. pylori infection. AI-based image classification has achieved a sensitivity of 92.2% in classifying endoscopic images. In colorectal cancer detection, AI-assisted computer vision applications, particularly CADe and CADx, have improved polyp detection rates. AI has also been shown to reduce adenoma miss rates and improve adenoma detection during colonoscopy. In inflammatory bowel disease (IBD), AI has demonstrated potential in diagnosing and managing conditions, with studies showing improved accuracy in detecting dysplasia and predicting adverse outcomes. AI can also enhance the detection of polypoid and non-polypoid dysplasia in IBD patients, potentially reducing colorectal cancer rates. In pancreatobiliary diseases, AI has shown promise in improving the diagnosis of malignant biliary strictures and cholangiocarcinoma, particularly when integrated with advanced endoscopic techniques like cholangioscopy and endoscopic ultrasound-guided fine-needle aspiration. Despite these advancements, AI faces challenges such as data standardization, ownership, and the need for continuous validation. AI's ability to generalize knowledge to new data and its integration with other technologies like radiomics and natural language processing are critical for its success. The future of AI in GI endoscopy lies in its potential to reduce procedure length, enhance diagnostic accuracy, and minimize the need for invasive testing. Collaboration between researchers, clinicians, and AI developers is essential to fully realize AI's potential in transforming gastroenterology.Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy by enhancing diagnostic accuracy, improving early detection, and enabling personalized treatment planning. AI applications, such as computer-aided detection (CADe) and computer-aided diagnosis (CADx), have significantly advanced GI endoscopy, particularly in conditions with low diagnostic sensitivity, like indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks (CNNs) improve diagnostic processes when integrated with cholangioscopy or endoscopic ultrasound, especially in detecting malignant biliary strictures and cholangiocarcinoma. AI's ability to analyze complex image data and provide real-time feedback streamlines endoscopic procedures, reduces the need for invasive biopsies, and decreases adverse events. However, challenges such as data quality and overfitting remain, requiring further research and validation. AI has shown promise in detecting early-stage gastric cancer, identifying Helicobacter pylori infections, and improving the detection of colorectal polyps and cancer. Studies indicate that AI systems can achieve high sensitivity and specificity in detecting gastric cancer and predicting H. pylori infection. AI-based image classification has achieved a sensitivity of 92.2% in classifying endoscopic images. In colorectal cancer detection, AI-assisted computer vision applications, particularly CADe and CADx, have improved polyp detection rates. AI has also been shown to reduce adenoma miss rates and improve adenoma detection during colonoscopy. In inflammatory bowel disease (IBD), AI has demonstrated potential in diagnosing and managing conditions, with studies showing improved accuracy in detecting dysplasia and predicting adverse outcomes. AI can also enhance the detection of polypoid and non-polypoid dysplasia in IBD patients, potentially reducing colorectal cancer rates. In pancreatobiliary diseases, AI has shown promise in improving the diagnosis of malignant biliary strictures and cholangiocarcinoma, particularly when integrated with advanced endoscopic techniques like cholangioscopy and endoscopic ultrasound-guided fine-needle aspiration. Despite these advancements, AI faces challenges such as data standardization, ownership, and the need for continuous validation. AI's ability to generalize knowledge to new data and its integration with other technologies like radiomics and natural language processing are critical for its success. The future of AI in GI endoscopy lies in its potential to reduce procedure length, enhance diagnostic accuracy, and minimize the need for invasive testing. Collaboration between researchers, clinicians, and AI developers is essential to fully realize AI's potential in transforming gastroenterology.